Overview

Dataset statistics

Number of variables43
Number of observations58053
Missing cells795333
Missing cells (%)31.9%
Total size in memory19.0 MiB
Average record size in memory344.0 B

Variable types

Text18
Numeric25

Alerts

pts_ot6_home has constant value ""Constant
pts_ot7_home has constant value ""Constant
pts_ot8_home has constant value ""Constant
pts_ot9_home has constant value ""Constant
pts_ot10_home has constant value ""Constant
pts_ot6_away has constant value ""Constant
pts_ot7_away has constant value ""Constant
pts_ot8_away has constant value ""Constant
pts_ot9_away has constant value ""Constant
pts_ot10_away has constant value ""Constant
game_sequence has 25532 (44.0%) missing valuesMissing
pts_qtr1_home has 1004 (1.7%) missing valuesMissing
pts_qtr2_home has 1013 (1.7%) missing valuesMissing
pts_qtr3_home has 1045 (1.8%) missing valuesMissing
pts_qtr4_home has 1044 (1.8%) missing valuesMissing
pts_ot1_home has 25759 (44.4%) missing valuesMissing
pts_ot2_home has 27051 (46.6%) missing valuesMissing
pts_ot3_home has 27243 (46.9%) missing valuesMissing
pts_ot4_home has 27270 (47.0%) missing valuesMissing
pts_ot5_home has 45577 (78.5%) missing valuesMissing
pts_ot6_home has 45578 (78.5%) missing valuesMissing
pts_ot7_home has 45578 (78.5%) missing valuesMissing
pts_ot8_home has 45578 (78.5%) missing valuesMissing
pts_ot9_home has 45578 (78.5%) missing valuesMissing
pts_ot10_home has 45578 (78.5%) missing valuesMissing
pts_qtr1_away has 1010 (1.7%) missing valuesMissing
pts_qtr2_away has 1013 (1.7%) missing valuesMissing
pts_qtr3_away has 1046 (1.8%) missing valuesMissing
pts_qtr4_away has 1046 (1.8%) missing valuesMissing
pts_ot1_away has 25759 (44.4%) missing valuesMissing
pts_ot2_away has 27051 (46.6%) missing valuesMissing
pts_ot3_away has 27243 (46.9%) missing valuesMissing
pts_ot4_away has 27270 (47.0%) missing valuesMissing
pts_ot5_away has 45577 (78.5%) missing valuesMissing
pts_ot6_away has 45578 (78.5%) missing valuesMissing
pts_ot7_away has 45578 (78.5%) missing valuesMissing
pts_ot8_away has 45578 (78.5%) missing valuesMissing
pts_ot9_away has 45578 (78.5%) missing valuesMissing
pts_ot10_away has 45578 (78.5%) missing valuesMissing
pts_ot3_home is highly skewed (γ1 = 23.75492179)Skewed
pts_ot4_home is highly skewed (γ1 = 49.53712063)Skewed
pts_ot5_home is highly skewed (γ1 = 111.696016)Skewed
pts_ot3_away is highly skewed (γ1 = 23.67367373)Skewed
pts_ot4_away is highly skewed (γ1 = 49.20775414)Skewed
pts_ot5_away is highly skewed (γ1 = 111.696016)Skewed
game_sequence has 2425 (4.2%) zerosZeros
pts_ot1_home has 29010 (50.0%) zerosZeros
pts_ot2_home has 30520 (52.6%) zerosZeros
pts_ot3_home has 30731 (52.9%) zerosZeros
pts_ot4_home has 30767 (53.0%) zerosZeros
pts_ot5_home has 12475 (21.5%) zerosZeros
pts_ot6_home has 12475 (21.5%) zerosZeros
pts_ot7_home has 12475 (21.5%) zerosZeros
pts_ot8_home has 12475 (21.5%) zerosZeros
pts_ot9_home has 12475 (21.5%) zerosZeros
pts_ot10_home has 12475 (21.5%) zerosZeros
pts_ot1_away has 29008 (50.0%) zerosZeros
pts_ot2_away has 30522 (52.6%) zerosZeros
pts_ot3_away has 30732 (52.9%) zerosZeros
pts_ot4_away has 30767 (53.0%) zerosZeros
pts_ot5_away has 12475 (21.5%) zerosZeros
pts_ot6_away has 12475 (21.5%) zerosZeros
pts_ot7_away has 12475 (21.5%) zerosZeros
pts_ot8_away has 12475 (21.5%) zerosZeros
pts_ot9_away has 12475 (21.5%) zerosZeros
pts_ot10_away has 12475 (21.5%) zerosZeros

Reproduction

Analysis started2023-07-13 14:05:36.397489
Analysis finished2023-07-13 14:05:38.070692
Duration1.67 second
Software versionydata-profiling vv4.3.1
Download configurationconfig.json

Variables

Distinct12610
Distinct (%)21.7%
Missing0
Missing (%)0.0%
Memory size453.7 KiB
2023-07-13T22:05:38.250892image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters1103007
Distinct characters13
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2160 ?
Unique (%)3.7%

Sample

1st row1946-11-01 00:00:00
2nd row1946-11-02 00:00:00
3rd row1946-11-02 00:00:00
4th row1946-11-02 00:00:00
5th row1946-11-02 00:00:00
ValueCountFrequency (%)
00:00:00 58040
50.0%
2023-04-09 15
 
< 0.1%
2021-05-16 15
 
< 0.1%
2006-04-19 14
 
< 0.1%
2014-04-16 14
 
< 0.1%
2015-10-28 14
 
< 0.1%
2009-01-02 14
 
< 0.1%
1996-11-01 14
 
< 0.1%
2007-04-18 14
 
< 0.1%
2018-11-23 14
 
< 0.1%
Other values (12592) 57938
49.9%
2023-07-13T22:05:38.520246image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 456449
41.4%
- 116106
 
10.5%
: 116106
 
10.5%
1 114297
 
10.4%
2 79993
 
7.3%
58053
 
5.3%
9 52919
 
4.8%
3 24128
 
2.2%
8 20606
 
1.9%
4 18335
 
1.7%
Other values (3) 46015
 
4.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 812742
73.7%
Dash Punctuation 116106
 
10.5%
Other Punctuation 116106
 
10.5%
Space Separator 58053
 
5.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 456449
56.2%
1 114297
 
14.1%
2 79993
 
9.8%
9 52919
 
6.5%
3 24128
 
3.0%
8 20606
 
2.5%
4 18335
 
2.3%
7 16275
 
2.0%
5 15752
 
1.9%
6 13988
 
1.7%
Dash Punctuation
ValueCountFrequency (%)
- 116106
100.0%
Other Punctuation
ValueCountFrequency (%)
: 116106
100.0%
Space Separator
ValueCountFrequency (%)
58053
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1103007
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 456449
41.4%
- 116106
 
10.5%
: 116106
 
10.5%
1 114297
 
10.4%
2 79993
 
7.3%
58053
 
5.3%
9 52919
 
4.8%
3 24128
 
2.2%
8 20606
 
1.9%
4 18335
 
1.7%
Other values (3) 46015
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1103007
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 456449
41.4%
- 116106
 
10.5%
: 116106
 
10.5%
1 114297
 
10.4%
2 79993
 
7.3%
58053
 
5.3%
9 52919
 
4.8%
3 24128
 
2.2%
8 20606
 
1.9%
4 18335
 
1.7%
Other values (3) 46015
 
4.2%

game_sequence
Real number (ℝ)

MISSING  ZEROS 

Distinct16
Distinct (%)< 0.1%
Missing25532
Missing (%)44.0%
Infinite0
Infinite (%)0.0%
Mean4.252759755
Minimum0
Maximum15
Zeros2425
Zeros (%)4.2%
Negative0
Negative (%)0.0%
Memory size453.7 KiB
2023-07-13T22:05:38.586518image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median4
Q36
95-th percentile10
Maximum15
Range15
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.061848695
Coefficient of variation (CV)0.7199674731
Kurtosis-0.3761139267
Mean4.252759755
Median Absolute Deviation (MAD)2
Skewness0.6146121367
Sum138304
Variance9.37491743
MonotonicityNot monotonic
2023-07-13T22:05:38.632795image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
1 4833
 
8.3%
2 4264
 
7.3%
3 3903
 
6.7%
4 3494
 
6.0%
5 3094
 
5.3%
6 2747
 
4.7%
0 2425
 
4.2%
7 2332
 
4.0%
8 1877
 
3.2%
9 1418
 
2.4%
Other values (6) 2134
 
3.7%
(Missing) 25532
44.0%
ValueCountFrequency (%)
0 2425
4.2%
1 4833
8.3%
2 4264
7.3%
3 3903
6.7%
4 3494
6.0%
ValueCountFrequency (%)
15 7
 
< 0.1%
14 40
 
0.1%
13 138
 
0.2%
12 319
0.5%
11 617
1.1%
Distinct58013
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Memory size453.7 KiB
2023-07-13T22:05:38.833487image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters580530
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique57973 ?
Unique (%)99.9%

Sample

1st row0024600001
2nd row0024600003
3rd row0024600002
4th row0024600004
5th row0024600005
ValueCountFrequency (%)
0032200001 2
 
< 0.1%
0039210001 2
 
< 0.1%
0037000001 2
 
< 0.1%
0035600001 2
 
< 0.1%
0030700001 2
 
< 0.1%
0039000002 2
 
< 0.1%
0035800001 2
 
< 0.1%
0035300004 2
 
< 0.1%
0035000001 2
 
< 0.1%
0031400001 2
 
< 0.1%
Other values (58003) 58033
> 99.9%
2023-07-13T22:05:39.087319image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 268092
46.2%
2 82014
 
14.1%
1 45817
 
7.9%
9 30704
 
5.3%
8 29751
 
5.1%
4 26988
 
4.6%
7 26454
 
4.6%
5 23904
 
4.1%
6 23539
 
4.1%
3 23267
 
4.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 580530
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 268092
46.2%
2 82014
 
14.1%
1 45817
 
7.9%
9 30704
 
5.3%
8 29751
 
5.1%
4 26988
 
4.6%
7 26454
 
4.6%
5 23904
 
4.1%
6 23539
 
4.1%
3 23267
 
4.0%

Most occurring scripts

ValueCountFrequency (%)
Common 580530
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 268092
46.2%
2 82014
 
14.1%
1 45817
 
7.9%
9 30704
 
5.3%
8 29751
 
5.1%
4 26988
 
4.6%
7 26454
 
4.6%
5 23904
 
4.1%
6 23539
 
4.1%
3 23267
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 580530
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 268092
46.2%
2 82014
 
14.1%
1 45817
 
7.9%
9 30704
 
5.3%
8 29751
 
5.1%
4 26988
 
4.6%
7 26454
 
4.6%
5 23904
 
4.1%
6 23539
 
4.1%
3 23267
 
4.0%
Distinct67
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size453.7 KiB
2023-07-13T22:05:39.266786image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length10
Median length10
Mean length9.99608978
Min length2

Characters and Unicode

Total characters580303
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique13 ?
Unique (%)< 0.1%

Sample

1st row1610610035
2nd row1610610034
3rd row1610612738
4th row1610610025
5th row1610610036
ValueCountFrequency (%)
1610612738 2709
 
4.7%
1610612747 2668
 
4.6%
1610612752 2626
 
4.5%
1610612744 2603
 
4.5%
1610612737 2568
 
4.4%
1610612755 2532
 
4.4%
1610612758 2492
 
4.3%
1610612765 2403
 
4.1%
1610612764 2130
 
3.7%
1610612756 2083
 
3.6%
Other values (57) 33239
57.3%
2023-07-13T22:05:39.503140image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 178966
30.8%
6 133120
22.9%
0 64185
 
11.1%
7 64111
 
11.0%
2 63476
 
10.9%
5 26406
 
4.6%
4 25564
 
4.4%
3 12126
 
2.1%
8 6680
 
1.2%
9 5669
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 580303
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 178966
30.8%
6 133120
22.9%
0 64185
 
11.1%
7 64111
 
11.0%
2 63476
 
10.9%
5 26406
 
4.6%
4 25564
 
4.4%
3 12126
 
2.1%
8 6680
 
1.2%
9 5669
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
Common 580303
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 178966
30.8%
6 133120
22.9%
0 64185
 
11.1%
7 64111
 
11.0%
2 63476
 
10.9%
5 26406
 
4.6%
4 25564
 
4.4%
3 12126
 
2.1%
8 6680
 
1.2%
9 5669
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 580303
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 178966
30.8%
6 133120
22.9%
0 64185
 
11.1%
7 64111
 
11.0%
2 63476
 
10.9%
5 26406
 
4.6%
4 25564
 
4.4%
3 12126
 
2.1%
8 6680
 
1.2%
9 5669
 
1.0%
Distinct103
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size453.7 KiB
2023-07-13T22:05:39.684532image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length3
Median length3
Mean length2.999552133
Min length2

Characters and Unicode

Total characters174133
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique17 ?
Unique (%)< 0.1%

Sample

1st rowHUS
2nd rowBOM
3rd rowBOS
4th rowCHS
5th rowWAS
ValueCountFrequency (%)
bos 2709
 
4.7%
nyk 2626
 
4.5%
lal 2272
 
3.9%
det 2138
 
3.7%
chi 2069
 
3.6%
phx 2041
 
3.5%
atl 2011
 
3.5%
mil 2000
 
3.4%
was 1903
 
3.3%
hou 1893
 
3.3%
Other values (93) 36391
62.7%
2023-07-13T22:05:39.915771image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 17480
 
10.0%
L 17176
 
9.9%
S 13808
 
7.9%
N 13214
 
7.6%
O 11744
 
6.7%
H 11571
 
6.6%
C 10200
 
5.9%
I 10040
 
5.8%
E 8320
 
4.8%
T 8000
 
4.6%
Other values (15) 52580
30.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 174133
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 17480
 
10.0%
L 17176
 
9.9%
S 13808
 
7.9%
N 13214
 
7.6%
O 11744
 
6.7%
H 11571
 
6.6%
C 10200
 
5.9%
I 10040
 
5.8%
E 8320
 
4.8%
T 8000
 
4.6%
Other values (15) 52580
30.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 174133
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 17480
 
10.0%
L 17176
 
9.9%
S 13808
 
7.9%
N 13214
 
7.6%
O 11744
 
6.7%
H 11571
 
6.6%
C 10200
 
5.9%
I 10040
 
5.8%
E 8320
 
4.8%
T 8000
 
4.6%
Other values (15) 52580
30.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 174133
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 17480
 
10.0%
L 17176
 
9.9%
S 13808
 
7.9%
N 13214
 
7.6%
O 11744
 
6.7%
H 11571
 
6.6%
C 10200
 
5.9%
I 10040
 
5.8%
E 8320
 
4.8%
T 8000
 
4.6%
Other values (15) 52580
30.2%
Distinct76
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size453.7 KiB
2023-07-13T22:05:40.090697image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length25
Median length17
Mean length8.433603776
Min length2

Characters and Unicode

Total characters489596
Distinct characters53
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique12 ?
Unique (%)< 0.1%

Sample

1st rowToronto
2nd rowSt. Louis
3rd rowBoston
4th rowChicago
5th rowWashington
ValueCountFrequency (%)
new 4803
 
6.6%
los 3388
 
4.6%
angeles 3388
 
4.6%
boston 2709
 
3.7%
york 2626
 
3.6%
philadelphia 2569
 
3.5%
san 2332
 
3.2%
chicago 2212
 
3.0%
detroit 2162
 
3.0%
milwaukee 2111
 
2.9%
Other values (80) 44614
61.2%
2023-07-13T22:05:40.319187image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 48301
 
9.9%
a 47657
 
9.7%
n 44058
 
9.0%
o 44014
 
9.0%
t 36386
 
7.4%
l 31493
 
6.4%
i 29161
 
6.0%
s 22417
 
4.6%
r 18329
 
3.7%
h 16211
 
3.3%
Other values (43) 151569
31.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 400072
81.7%
Uppercase Letter 73677
 
15.0%
Space Separator 14861
 
3.0%
Other Punctuation 814
 
0.2%
Dash Punctuation 172
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 48301
12.1%
a 47657
11.9%
n 44058
11.0%
o 44014
11.0%
t 36386
9.1%
l 31493
7.9%
i 29161
7.3%
s 22417
 
5.6%
r 18329
 
4.6%
h 16211
 
4.1%
Other values (14) 62045
15.5%
Uppercase Letter
ValueCountFrequency (%)
S 8105
11.0%
A 7714
10.5%
C 6927
9.4%
P 6629
9.0%
M 5998
 
8.1%
D 5973
 
8.1%
N 4907
 
6.7%
L 4186
 
5.7%
B 3928
 
5.3%
O 2911
 
4.0%
Other values (14) 16399
22.3%
Other Punctuation
ValueCountFrequency (%)
. 746
91.6%
/ 67
 
8.2%
' 1
 
0.1%
Space Separator
ValueCountFrequency (%)
14861
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 172
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 473749
96.8%
Common 15847
 
3.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 48301
 
10.2%
a 47657
 
10.1%
n 44058
 
9.3%
o 44014
 
9.3%
t 36386
 
7.7%
l 31493
 
6.6%
i 29161
 
6.2%
s 22417
 
4.7%
r 18329
 
3.9%
h 16211
 
3.4%
Other values (38) 135722
28.6%
Common
ValueCountFrequency (%)
14861
93.8%
. 746
 
4.7%
- 172
 
1.1%
/ 67
 
0.4%
' 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 489596
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 48301
 
9.9%
a 47657
 
9.7%
n 44058
 
9.0%
o 44014
 
9.0%
t 36386
 
7.4%
l 31493
 
6.4%
i 29161
 
6.0%
s 22417
 
4.6%
r 18329
 
3.7%
h 16211
 
3.3%
Other values (43) 151569
31.0%
Distinct79
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size453.7 KiB
2023-07-13T22:05:40.487809image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length22
Median length16
Mean length6.705786092
Min length4

Characters and Unicode

Total characters389291
Distinct characters52
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique18 ?
Unique (%)< 0.1%

Sample

1st rowHuskies
2nd rowBombers
3rd rowCeltics
4th rowStags
5th rowCapitols
ValueCountFrequency (%)
celtics 2709
 
4.5%
lakers 2668
 
4.4%
knicks 2626
 
4.4%
warriors 2603
 
4.3%
hawks 2529
 
4.2%
pistons 2403
 
4.0%
76ers 2129
 
3.6%
suns 2083
 
3.5%
bulls 2069
 
3.5%
bucks 2000
 
3.3%
Other values (81) 36152
60.3%
2023-07-13T22:05:40.711585image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
s 55526
14.3%
r 33285
 
8.6%
e 32556
 
8.4%
a 28164
 
7.2%
i 27488
 
7.1%
l 21099
 
5.4%
t 16867
 
4.3%
c 16277
 
4.2%
o 13785
 
3.5%
k 13697
 
3.5%
Other values (42) 130547
33.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 323802
83.2%
Uppercase Letter 59309
 
15.2%
Decimal Number 4260
 
1.1%
Space Separator 1918
 
0.5%
Other Punctuation 1
 
< 0.1%
Dash Punctuation 1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s 55526
17.1%
r 33285
10.3%
e 32556
10.1%
a 28164
8.7%
i 27488
8.5%
l 21099
 
6.5%
t 16867
 
5.2%
c 16277
 
5.0%
o 13785
 
4.3%
k 13697
 
4.2%
Other values (14) 65058
20.1%
Uppercase Letter
ValueCountFrequency (%)
B 8001
13.5%
S 6921
11.7%
C 6357
10.7%
H 5164
8.7%
P 4520
7.6%
K 4459
7.5%
N 3988
6.7%
R 3765
6.3%
T 3761
6.3%
W 3624
6.1%
Other values (12) 8749
14.8%
Decimal Number
ValueCountFrequency (%)
6 2130
50.0%
7 2129
50.0%
3 1
 
< 0.1%
Space Separator
ValueCountFrequency (%)
1918
100.0%
Other Punctuation
ValueCountFrequency (%)
' 1
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 383111
98.4%
Common 6180
 
1.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
s 55526
14.5%
r 33285
 
8.7%
e 32556
 
8.5%
a 28164
 
7.4%
i 27488
 
7.2%
l 21099
 
5.5%
t 16867
 
4.4%
c 16277
 
4.2%
o 13785
 
3.6%
k 13697
 
3.6%
Other values (36) 124367
32.5%
Common
ValueCountFrequency (%)
6 2130
34.5%
7 2129
34.4%
1918
31.0%
3 1
 
< 0.1%
' 1
 
< 0.1%
- 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 389291
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
s 55526
14.3%
r 33285
 
8.6%
e 32556
 
8.4%
a 28164
 
7.2%
i 27488
 
7.1%
l 21099
 
5.4%
t 16867
 
4.3%
c 16277
 
4.2%
o 13785
 
3.5%
k 13697
 
3.5%
Other values (42) 130547
33.5%
Distinct2505
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Memory size453.7 KiB
2023-07-13T22:05:40.994109image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length5
Median length1
Mean length2.390849741
Min length1

Characters and Unicode

Total characters138796
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique340 ?
Unique (%)0.6%

Sample

1st row-
2nd row-
3rd row-
4th row-
5th row-
ValueCountFrequency (%)
32781
56.5%
0-1 591
 
1.0%
1-0 590
 
1.0%
1-1 529
 
0.9%
2-1 403
 
0.7%
1-2 396
 
0.7%
2-2 370
 
0.6%
2-0 331
 
0.6%
3-2 290
 
0.5%
0-2 282
 
0.5%
Other values (2495) 21490
37.0%
2023-07-13T22:05:41.331208image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 58053
41.8%
1 18160
 
13.1%
2 15989
 
11.5%
3 11872
 
8.6%
4 8144
 
5.9%
0 5887
 
4.2%
5 5283
 
3.8%
6 4085
 
2.9%
7 3862
 
2.8%
8 3801
 
2.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 80743
58.2%
Dash Punctuation 58053
41.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 18160
22.5%
2 15989
19.8%
3 11872
14.7%
4 8144
10.1%
0 5887
 
7.3%
5 5283
 
6.5%
6 4085
 
5.1%
7 3862
 
4.8%
8 3801
 
4.7%
9 3660
 
4.5%
Dash Punctuation
ValueCountFrequency (%)
- 58053
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 138796
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 58053
41.8%
1 18160
 
13.1%
2 15989
 
11.5%
3 11872
 
8.6%
4 8144
 
5.9%
0 5887
 
4.2%
5 5283
 
3.8%
6 4085
 
2.9%
7 3862
 
2.8%
8 3801
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 138796
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 58053
41.8%
1 18160
 
13.1%
2 15989
 
11.5%
3 11872
 
8.6%
4 8144
 
5.9%
0 5887
 
4.2%
5 5283
 
3.8%
6 4085
 
2.9%
7 3862
 
2.8%
8 3801
 
2.7%

pts_qtr1_home
Text

MISSING 

Distinct57
Distinct (%)0.1%
Missing1004
Missing (%)1.7%
Memory size453.7 KiB
2023-07-13T22:05:41.466221image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length4
Median length2
Mean length1.998597697
Min length1

Characters and Unicode

Total characters114018
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10 ?
Unique (%)< 0.1%

Sample

1st row16
2nd row10.0
3rd row21
4th row13
5th row21
ValueCountFrequency (%)
26 3934
 
6.9%
27 3818
 
6.7%
24 3767
 
6.6%
25 3742
 
6.6%
28 3643
 
6.4%
23 3395
 
6.0%
29 3335
 
5.8%
22 3265
 
5.7%
30 2900
 
5.1%
21 2762
 
4.8%
Other values (47) 22488
39.4%
2023-07-13T22:05:41.645844image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 40063
35.1%
3 19914
17.5%
1 13177
 
11.6%
4 6482
 
5.7%
6 5904
 
5.2%
8 5841
 
5.1%
0 5785
 
5.1%
9 5701
 
5.0%
7 5628
 
4.9%
5 5517
 
4.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 114012
> 99.9%
Other Punctuation 6
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 40063
35.1%
3 19914
17.5%
1 13177
 
11.6%
4 6482
 
5.7%
6 5904
 
5.2%
8 5841
 
5.1%
0 5785
 
5.1%
9 5701
 
5.0%
7 5628
 
4.9%
5 5517
 
4.8%
Other Punctuation
ValueCountFrequency (%)
. 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 114018
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 40063
35.1%
3 19914
17.5%
1 13177
 
11.6%
4 6482
 
5.7%
6 5904
 
5.2%
8 5841
 
5.1%
0 5785
 
5.1%
9 5701
 
5.0%
7 5628
 
4.9%
5 5517
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 114018
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 40063
35.1%
3 19914
17.5%
1 13177
 
11.6%
4 6482
 
5.7%
6 5904
 
5.2%
8 5841
 
5.1%
0 5785
 
5.1%
9 5701
 
5.0%
7 5628
 
4.9%
5 5517
 
4.8%

pts_qtr2_home
Text

MISSING 

Distinct53
Distinct (%)0.1%
Missing1013
Missing (%)1.7%
Memory size453.7 KiB
2023-07-13T22:05:41.775703image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length4
Median length2
Mean length1.998597475
Min length1

Characters and Unicode

Total characters114000
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)< 0.1%

Sample

1st row16
2nd row16
3rd row4
4th row20
5th row14
ValueCountFrequency (%)
26 3950
 
6.9%
25 3891
 
6.8%
24 3867
 
6.8%
27 3648
 
6.4%
23 3598
 
6.3%
28 3528
 
6.2%
22 3451
 
6.1%
29 3293
 
5.8%
21 2907
 
5.1%
30 2869
 
5.0%
Other values (43) 22038
38.6%
2023-07-13T22:05:41.956288image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 40520
35.5%
3 18944
16.6%
1 13854
 
12.2%
4 6442
 
5.7%
6 5813
 
5.1%
0 5792
 
5.1%
9 5729
 
5.0%
5 5670
 
5.0%
8 5645
 
5.0%
7 5590
 
4.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 113999
> 99.9%
Other Punctuation 1
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 40520
35.5%
3 18944
16.6%
1 13854
 
12.2%
4 6442
 
5.7%
6 5813
 
5.1%
0 5792
 
5.1%
9 5729
 
5.0%
5 5670
 
5.0%
8 5645
 
5.0%
7 5590
 
4.9%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 114000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 40520
35.5%
3 18944
16.6%
1 13854
 
12.2%
4 6442
 
5.7%
6 5813
 
5.1%
0 5792
 
5.1%
9 5729
 
5.0%
5 5670
 
5.0%
8 5645
 
5.0%
7 5590
 
4.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 114000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 40520
35.5%
3 18944
16.6%
1 13854
 
12.2%
4 6442
 
5.7%
6 5813
 
5.1%
0 5792
 
5.1%
9 5729
 
5.0%
5 5670
 
5.0%
8 5645
 
5.0%
7 5590
 
4.9%

pts_qtr3_home
Text

MISSING 

Distinct55
Distinct (%)0.1%
Missing1045
Missing (%)1.8%
Memory size453.7 KiB
2023-07-13T22:05:42.081653image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length4
Median length2
Mean length1.998316026
Min length1

Characters and Unicode

Total characters113920
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8 ?
Unique (%)< 0.1%

Sample

1st row18
2nd row14
3rd row12
4th row13
5th row19
ValueCountFrequency (%)
24 3858
 
6.8%
26 3819
 
6.7%
25 3761
 
6.6%
27 3604
 
6.3%
23 3582
 
6.3%
28 3490
 
6.1%
22 3387
 
5.9%
29 3123
 
5.5%
21 2869
 
5.0%
30 2851
 
5.0%
Other values (45) 22664
39.8%
2023-07-13T22:05:42.267330image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 39877
35.0%
3 19422
17.0%
1 14058
 
12.3%
4 6537
 
5.7%
8 5785
 
5.1%
0 5768
 
5.1%
6 5757
 
5.1%
9 5600
 
4.9%
7 5584
 
4.9%
5 5529
 
4.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 113917
> 99.9%
Other Punctuation 3
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 39877
35.0%
3 19422
17.0%
1 14058
 
12.3%
4 6537
 
5.7%
8 5785
 
5.1%
0 5768
 
5.1%
6 5757
 
5.1%
9 5600
 
4.9%
7 5584
 
4.9%
5 5529
 
4.9%
Other Punctuation
ValueCountFrequency (%)
. 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 113920
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 39877
35.0%
3 19422
17.0%
1 14058
 
12.3%
4 6537
 
5.7%
8 5785
 
5.1%
0 5768
 
5.1%
6 5757
 
5.1%
9 5600
 
4.9%
7 5584
 
4.9%
5 5529
 
4.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 113920
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 39877
35.0%
3 19422
17.0%
1 14058
 
12.3%
4 6537
 
5.7%
8 5785
 
5.1%
0 5768
 
5.1%
6 5757
 
5.1%
9 5600
 
4.9%
7 5584
 
4.9%
5 5529
 
4.9%

pts_qtr4_home
Text

MISSING 

Distinct54
Distinct (%)0.1%
Missing1044
Missing (%)1.8%
Memory size453.7 KiB
2023-07-13T22:05:42.395155image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length4
Median length2
Mean length1.997807364
Min length1

Characters and Unicode

Total characters113893
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st row6
2nd row13
3rd row13
4th row9
5th row14
ValueCountFrequency (%)
25 3745
 
6.6%
26 3741
 
6.6%
24 3687
 
6.5%
23 3498
 
6.1%
27 3488
 
6.1%
28 3327
 
5.8%
22 3311
 
5.8%
29 3107
 
5.5%
30 2851
 
5.0%
21 2765
 
4.9%
Other values (44) 23489
41.2%
2023-07-13T22:05:42.575377image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 39030
34.3%
3 19611
17.2%
1 14432
 
12.7%
4 6584
 
5.8%
0 5838
 
5.1%
6 5789
 
5.1%
9 5704
 
5.0%
5 5683
 
5.0%
8 5668
 
5.0%
7 5552
 
4.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 113891
> 99.9%
Other Punctuation 2
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 39030
34.3%
3 19611
17.2%
1 14432
 
12.7%
4 6584
 
5.8%
0 5838
 
5.1%
6 5789
 
5.1%
9 5704
 
5.0%
5 5683
 
5.0%
8 5668
 
5.0%
7 5552
 
4.9%
Other Punctuation
ValueCountFrequency (%)
. 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 113893
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 39030
34.3%
3 19611
17.2%
1 14432
 
12.7%
4 6584
 
5.8%
0 5838
 
5.1%
6 5789
 
5.1%
9 5704
 
5.0%
5 5683
 
5.0%
8 5668
 
5.0%
7 5552
 
4.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 113893
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 39030
34.3%
3 19611
17.2%
1 14432
 
12.7%
4 6584
 
5.8%
0 5838
 
5.1%
6 5789
 
5.1%
9 5704
 
5.0%
5 5683
 
5.0%
8 5668
 
5.0%
7 5552
 
4.9%

pts_ot1_home
Real number (ℝ)

MISSING  ZEROS 

Distinct26
Distinct (%)0.1%
Missing25759
Missing (%)44.4%
Infinite0
Infinite (%)0.0%
Mean1.04236081
Minimum0
Maximum25
Zeros29010
Zeros (%)50.0%
Negative0
Negative (%)0.0%
Memory size453.7 KiB
2023-07-13T22:05:42.637000image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile10
Maximum25
Range25
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3.324453978
Coefficient of variation (CV)3.189350507
Kurtosis10.08809161
Mean1.04236081
Median Absolute Deviation (MAD)0
Skewness3.275155232
Sum33662
Variance11.05199425
MonotonicityNot monotonic
2023-07-13T22:05:42.689097image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
0 29010
50.0%
8 359
 
0.6%
10 325
 
0.6%
9 317
 
0.5%
11 316
 
0.5%
12 290
 
0.5%
7 253
 
0.4%
6 248
 
0.4%
13 240
 
0.4%
14 202
 
0.3%
Other values (16) 734
 
1.3%
(Missing) 25759
44.4%
ValueCountFrequency (%)
0 29010
50.0%
1 1
 
< 0.1%
2 38
 
0.1%
3 29
 
< 0.1%
4 121
 
0.2%
ValueCountFrequency (%)
25 1
 
< 0.1%
24 1
 
< 0.1%
23 4
 
< 0.1%
22 3
 
< 0.1%
21 11
< 0.1%

pts_ot2_home
Real number (ℝ)

MISSING  ZEROS 

Distinct22
Distinct (%)0.1%
Missing27051
Missing (%)46.6%
Infinite0
Infinite (%)0.0%
Mean0.1506031869
Minimum0
Maximum22
Zeros30520
Zeros (%)52.6%
Negative0
Negative (%)0.0%
Memory size453.7 KiB
2023-07-13T22:05:42.739934image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum22
Range22
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.279468094
Coefficient of variation (CV)8.495624296
Kurtosis92.13602699
Mean0.1506031869
Median Absolute Deviation (MAD)0
Skewness9.290540734
Sum4669
Variance1.637038603
MonotonicityNot monotonic
2023-07-13T22:05:42.786847image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
0 30520
52.6%
10 57
 
0.1%
9 49
 
0.1%
11 48
 
0.1%
8 47
 
0.1%
7 45
 
0.1%
6 45
 
0.1%
12 44
 
0.1%
13 32
 
0.1%
4 20
 
< 0.1%
Other values (12) 95
 
0.2%
(Missing) 27051
46.6%
ValueCountFrequency (%)
0 30520
52.6%
1 1
 
< 0.1%
2 7
 
< 0.1%
3 6
 
< 0.1%
4 20
 
< 0.1%
ValueCountFrequency (%)
22 1
 
< 0.1%
20 2
 
< 0.1%
19 3
< 0.1%
18 6
< 0.1%
17 7
< 0.1%

pts_ot3_home
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct16
Distinct (%)0.1%
Missing27243
Missing (%)46.9%
Infinite0
Infinite (%)0.0%
Mean0.02301200909
Minimum0
Maximum19
Zeros30731
Zeros (%)52.9%
Negative0
Negative (%)0.0%
Memory size453.7 KiB
2023-07-13T22:05:42.833151image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum19
Range19
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.4883891415
Coefficient of variation (CV)21.22322913
Kurtosis614.3651452
Mean0.02301200909
Median Absolute Deviation (MAD)0
Skewness23.75492179
Sum709
Variance0.2385239536
MonotonicityNot monotonic
2023-07-13T22:05:42.878756image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0 30731
52.9%
8 9
 
< 0.1%
4 9
 
< 0.1%
11 9
 
< 0.1%
9 8
 
< 0.1%
5 8
 
< 0.1%
10 7
 
< 0.1%
6 6
 
< 0.1%
7 6
 
< 0.1%
13 5
 
< 0.1%
Other values (6) 12
 
< 0.1%
(Missing) 27243
46.9%
ValueCountFrequency (%)
0 30731
52.9%
4 9
 
< 0.1%
5 8
 
< 0.1%
6 6
 
< 0.1%
7 6
 
< 0.1%
ValueCountFrequency (%)
19 1
 
< 0.1%
17 1
 
< 0.1%
16 1
 
< 0.1%
15 3
< 0.1%
14 4
< 0.1%

pts_ot4_home
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct10
Distinct (%)< 0.1%
Missing27270
Missing (%)47.0%
Infinite0
Infinite (%)0.0%
Mean0.00506773219
Minimum0
Maximum17
Zeros30767
Zeros (%)53.0%
Negative0
Negative (%)0.0%
Memory size453.7 KiB
2023-07-13T22:05:42.927264image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum17
Range17
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.233005956
Coefficient of variation (CV)45.97834835
Kurtosis2616.677896
Mean0.00506773219
Median Absolute Deviation (MAD)0
Skewness49.53712063
Sum156
Variance0.05429177551
MonotonicityNot monotonic
2023-07-13T22:05:42.970575image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0 30767
53.0%
12 4
 
< 0.1%
10 4
 
< 0.1%
8 2
 
< 0.1%
4 1
 
< 0.1%
5 1
 
< 0.1%
9 1
 
< 0.1%
11 1
 
< 0.1%
17 1
 
< 0.1%
6 1
 
< 0.1%
(Missing) 27270
47.0%
ValueCountFrequency (%)
0 30767
53.0%
4 1
 
< 0.1%
5 1
 
< 0.1%
6 1
 
< 0.1%
8 2
 
< 0.1%
ValueCountFrequency (%)
17 1
 
< 0.1%
12 4
< 0.1%
11 1
 
< 0.1%
10 4
< 0.1%
9 1
 
< 0.1%

pts_ot5_home
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct2
Distinct (%)< 0.1%
Missing45577
Missing (%)78.5%
Infinite0
Infinite (%)0.0%
Mean0.001282462328
Minimum0
Maximum16
Zeros12475
Zeros (%)21.5%
Negative0
Negative (%)0.0%
Memory size453.7 KiB
2023-07-13T22:05:43.015820image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum16
Range16
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.1432459327
Coefficient of variation (CV)111.696016
Kurtosis12476
Mean0.001282462328
Median Absolute Deviation (MAD)0
Skewness111.696016
Sum16
Variance0.02051939724
MonotonicityDecreasing
2023-07-13T22:05:43.056548image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
0 12475
 
21.5%
16 1
 
< 0.1%
(Missing) 45577
78.5%
ValueCountFrequency (%)
0 12475
21.5%
16 1
 
< 0.1%
ValueCountFrequency (%)
16 1
 
< 0.1%
0 12475
21.5%

pts_ot6_home
Real number (ℝ)

CONSTANT  MISSING  ZEROS 

Distinct1
Distinct (%)< 0.1%
Missing45578
Missing (%)78.5%
Infinite0
Infinite (%)0.0%
Mean0
Minimum0
Maximum0
Zeros12475
Zeros (%)21.5%
Negative0
Negative (%)0.0%
Memory size453.7 KiB
2023-07-13T22:05:43.103226image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum0
Range0
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0
Coefficient of variation (CV)nan
Kurtosis0
Mean0
Median Absolute Deviation (MAD)0
Skewness0
Sum0
Variance0
MonotonicityIncreasing
2023-07-13T22:05:43.142221image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=1)
ValueCountFrequency (%)
0 12475
 
21.5%
(Missing) 45578
78.5%
ValueCountFrequency (%)
0 12475
21.5%
ValueCountFrequency (%)
0 12475
21.5%

pts_ot7_home
Real number (ℝ)

CONSTANT  MISSING  ZEROS 

Distinct1
Distinct (%)< 0.1%
Missing45578
Missing (%)78.5%
Infinite0
Infinite (%)0.0%
Mean0
Minimum0
Maximum0
Zeros12475
Zeros (%)21.5%
Negative0
Negative (%)0.0%
Memory size453.7 KiB
2023-07-13T22:05:43.183349image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum0
Range0
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0
Coefficient of variation (CV)nan
Kurtosis0
Mean0
Median Absolute Deviation (MAD)0
Skewness0
Sum0
Variance0
MonotonicityIncreasing
2023-07-13T22:05:43.222705image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=1)
ValueCountFrequency (%)
0 12475
 
21.5%
(Missing) 45578
78.5%
ValueCountFrequency (%)
0 12475
21.5%
ValueCountFrequency (%)
0 12475
21.5%

pts_ot8_home
Real number (ℝ)

CONSTANT  MISSING  ZEROS 

Distinct1
Distinct (%)< 0.1%
Missing45578
Missing (%)78.5%
Infinite0
Infinite (%)0.0%
Mean0
Minimum0
Maximum0
Zeros12475
Zeros (%)21.5%
Negative0
Negative (%)0.0%
Memory size453.7 KiB
2023-07-13T22:05:43.263242image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum0
Range0
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0
Coefficient of variation (CV)nan
Kurtosis0
Mean0
Median Absolute Deviation (MAD)0
Skewness0
Sum0
Variance0
MonotonicityIncreasing
2023-07-13T22:05:43.302583image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=1)
ValueCountFrequency (%)
0 12475
 
21.5%
(Missing) 45578
78.5%
ValueCountFrequency (%)
0 12475
21.5%
ValueCountFrequency (%)
0 12475
21.5%

pts_ot9_home
Real number (ℝ)

CONSTANT  MISSING  ZEROS 

Distinct1
Distinct (%)< 0.1%
Missing45578
Missing (%)78.5%
Infinite0
Infinite (%)0.0%
Mean0
Minimum0
Maximum0
Zeros12475
Zeros (%)21.5%
Negative0
Negative (%)0.0%
Memory size453.7 KiB
2023-07-13T22:05:43.342987image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum0
Range0
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0
Coefficient of variation (CV)nan
Kurtosis0
Mean0
Median Absolute Deviation (MAD)0
Skewness0
Sum0
Variance0
MonotonicityIncreasing
2023-07-13T22:05:43.382394image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=1)
ValueCountFrequency (%)
0 12475
 
21.5%
(Missing) 45578
78.5%
ValueCountFrequency (%)
0 12475
21.5%
ValueCountFrequency (%)
0 12475
21.5%

pts_ot10_home
Real number (ℝ)

CONSTANT  MISSING  ZEROS 

Distinct1
Distinct (%)< 0.1%
Missing45578
Missing (%)78.5%
Infinite0
Infinite (%)0.0%
Mean0
Minimum0
Maximum0
Zeros12475
Zeros (%)21.5%
Negative0
Negative (%)0.0%
Memory size453.7 KiB
2023-07-13T22:05:43.423038image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum0
Range0
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0
Coefficient of variation (CV)nan
Kurtosis0
Mean0
Median Absolute Deviation (MAD)0
Skewness0
Sum0
Variance0
MonotonicityIncreasing
2023-07-13T22:05:43.462342image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=1)
ValueCountFrequency (%)
0 12475
 
21.5%
(Missing) 45578
78.5%
ValueCountFrequency (%)
0 12475
21.5%
ValueCountFrequency (%)
0 12475
21.5%

pts_home
Real number (ℝ)

Distinct132
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean102.966565
Minimum19
Maximum196
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size453.7 KiB
2023-07-13T22:05:43.514655image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum19
5-th percentile79
Q193
median103
Q3113
95-th percentile127
Maximum196
Range177
Interquartile range (IQR)20

Descriptive statistics

Standard deviation14.68646228
Coefficient of variation (CV)0.1426333128
Kurtosis0.3758174488
Mean102.966565
Median Absolute Deviation (MAD)10
Skewness0.02144560425
Sum5977518
Variance215.6921743
MonotonicityNot monotonic
2023-07-13T22:05:43.575532image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
103 1670
 
2.9%
99 1636
 
2.8%
101 1626
 
2.8%
105 1624
 
2.8%
102 1614
 
2.8%
104 1597
 
2.8%
106 1594
 
2.7%
100 1571
 
2.7%
107 1542
 
2.7%
98 1537
 
2.6%
Other values (122) 42042
72.4%
ValueCountFrequency (%)
19 1
< 0.1%
36 1
< 0.1%
40 1
< 0.1%
43 1
< 0.1%
44 1
< 0.1%
ValueCountFrequency (%)
196 1
< 0.1%
192 2
< 0.1%
184 1
< 0.1%
175 2
< 0.1%
173 1
< 0.1%
Distinct75
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size453.7 KiB
2023-07-13T22:05:43.746045image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length10
Median length10
Mean length9.994883985
Min length2

Characters and Unicode

Total characters580233
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique15 ?
Unique (%)< 0.1%

Sample

1st row1610612752
2nd row1610610031
3rd row1610610032
4th row1610612752
5th row1610610028
ValueCountFrequency (%)
1610612738 2704
 
4.7%
1610612747 2645
 
4.6%
1610612755 2627
 
4.5%
1610612765 2592
 
4.5%
1610612744 2587
 
4.5%
1610612752 2542
 
4.4%
1610612737 2469
 
4.3%
1610612758 2428
 
4.2%
1610612764 2102
 
3.6%
1610612745 2038
 
3.5%
Other values (65) 33319
57.4%
2023-07-13T22:05:43.980490image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 178851
30.8%
6 133122
22.9%
7 64048
 
11.0%
0 64037
 
11.0%
2 63533
 
10.9%
5 26669
 
4.6%
4 25575
 
4.4%
3 11893
 
2.0%
8 6713
 
1.2%
9 5792
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 580233
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 178851
30.8%
6 133122
22.9%
7 64048
 
11.0%
0 64037
 
11.0%
2 63533
 
10.9%
5 26669
 
4.6%
4 25575
 
4.4%
3 11893
 
2.0%
8 6713
 
1.2%
9 5792
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
Common 580233
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 178851
30.8%
6 133122
22.9%
7 64048
 
11.0%
0 64037
 
11.0%
2 63533
 
10.9%
5 26669
 
4.6%
4 25575
 
4.4%
3 11893
 
2.0%
8 6713
 
1.2%
9 5792
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 580233
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 178851
30.8%
6 133122
22.9%
7 64048
 
11.0%
0 64037
 
11.0%
2 63533
 
10.9%
5 26669
 
4.6%
4 25575
 
4.4%
3 11893
 
2.0%
8 6713
 
1.2%
9 5792
 
1.0%
Distinct109
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size453.7 KiB
2023-07-13T22:05:44.155651image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length3
Median length3
Mean length2.999552133
Min length2

Characters and Unicode

Total characters174133
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique19 ?
Unique (%)< 0.1%

Sample

1st rowNYK
2nd rowPIT
3rd rowPRO
4th rowNYK
5th rowDEF
ValueCountFrequency (%)
bos 2704
 
4.7%
nyk 2542
 
4.4%
det 2302
 
4.0%
lal 2259
 
3.9%
mil 2017
 
3.5%
chi 2013
 
3.5%
atl 1941
 
3.3%
hou 1931
 
3.3%
por 1920
 
3.3%
cle 1911
 
3.3%
Other values (99) 36513
62.9%
2023-07-13T22:05:44.377354image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 17571
 
10.1%
L 17302
 
9.9%
S 13765
 
7.9%
N 13012
 
7.5%
O 11896
 
6.8%
H 11465
 
6.6%
I 10168
 
5.8%
C 10091
 
5.8%
E 8290
 
4.8%
T 8224
 
4.7%
Other values (15) 52349
30.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 174133
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 17571
 
10.1%
L 17302
 
9.9%
S 13765
 
7.9%
N 13012
 
7.5%
O 11896
 
6.8%
H 11465
 
6.6%
I 10168
 
5.8%
C 10091
 
5.8%
E 8290
 
4.8%
T 8224
 
4.7%
Other values (15) 52349
30.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 174133
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 17571
 
10.1%
L 17302
 
9.9%
S 13765
 
7.9%
N 13012
 
7.5%
O 11896
 
6.8%
H 11465
 
6.6%
I 10168
 
5.8%
C 10091
 
5.8%
E 8290
 
4.8%
T 8224
 
4.7%
Other values (15) 52349
30.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 174133
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 17571
 
10.1%
L 17302
 
9.9%
S 13765
 
7.9%
N 13012
 
7.5%
O 11896
 
6.8%
H 11465
 
6.6%
I 10168
 
5.8%
C 10091
 
5.8%
E 8290
 
4.8%
T 8224
 
4.7%
Other values (15) 52349
30.1%
Distinct84
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size453.7 KiB
2023-07-13T22:05:44.566514image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length25
Median length17
Mean length8.431106058
Min length2

Characters and Unicode

Total characters489451
Distinct characters53
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique14 ?
Unique (%)< 0.1%

Sample

1st rowNew York
2nd rowPittsburgh
3rd rowProvidence
4th rowNew York
5th rowDetroit
ValueCountFrequency (%)
new 4748
 
6.5%
los 3445
 
4.7%
angeles 3445
 
4.7%
boston 2704
 
3.7%
philadelphia 2642
 
3.6%
york 2542
 
3.5%
san 2399
 
3.3%
detroit 2322
 
3.2%
milwaukee 2149
 
2.9%
chicago 2146
 
2.9%
Other values (89) 44362
60.8%
2023-07-13T22:05:44.820963image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 48369
 
9.9%
a 47722
 
9.8%
n 44067
 
9.0%
o 44012
 
9.0%
t 36557
 
7.5%
l 31731
 
6.5%
i 29232
 
6.0%
s 22319
 
4.6%
r 18368
 
3.8%
h 16052
 
3.3%
Other values (43) 151022
30.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 399940
81.7%
Uppercase Letter 73683
 
15.1%
Space Separator 14851
 
3.0%
Other Punctuation 816
 
0.2%
Dash Punctuation 161
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 48369
12.1%
a 47722
11.9%
n 44067
11.0%
o 44012
11.0%
t 36557
9.1%
l 31731
7.9%
i 29232
7.3%
s 22319
 
5.6%
r 18368
 
4.6%
h 16052
 
4.0%
Other values (14) 61511
15.4%
Uppercase Letter
ValueCountFrequency (%)
S 8103
11.0%
A 7783
10.6%
C 6753
9.2%
P 6611
9.0%
M 6064
 
8.2%
D 6011
 
8.2%
N 4852
 
6.6%
L 4232
 
5.7%
B 3862
 
5.2%
O 2965
 
4.0%
Other values (14) 16447
22.3%
Other Punctuation
ValueCountFrequency (%)
. 741
90.8%
/ 73
 
8.9%
' 2
 
0.2%
Space Separator
ValueCountFrequency (%)
14851
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 161
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 473623
96.8%
Common 15828
 
3.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 48369
 
10.2%
a 47722
 
10.1%
n 44067
 
9.3%
o 44012
 
9.3%
t 36557
 
7.7%
l 31731
 
6.7%
i 29232
 
6.2%
s 22319
 
4.7%
r 18368
 
3.9%
h 16052
 
3.4%
Other values (38) 135194
28.5%
Common
ValueCountFrequency (%)
14851
93.8%
. 741
 
4.7%
- 161
 
1.0%
/ 73
 
0.5%
' 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 489451
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 48369
 
9.9%
a 47722
 
9.8%
n 44067
 
9.0%
o 44012
 
9.0%
t 36557
 
7.5%
l 31731
 
6.5%
i 29232
 
6.0%
s 22319
 
4.6%
r 18368
 
3.8%
h 16052
 
3.3%
Other values (43) 151022
30.9%
Distinct85
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size453.7 KiB
2023-07-13T22:05:44.993590image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length25
Median length19
Mean length6.708507743
Min length2

Characters and Unicode

Total characters389449
Distinct characters52
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique20 ?
Unique (%)< 0.1%

Sample

1st rowKnicks
2nd rowIronmen
3rd rowSteamrollers
4th rowKnicks
5th rowFalcons
ValueCountFrequency (%)
celtics 2704
 
4.5%
lakers 2645
 
4.4%
pistons 2592
 
4.3%
warriors 2587
 
4.3%
knicks 2542
 
4.2%
hawks 2443
 
4.1%
76ers 2222
 
3.7%
rockets 2038
 
3.4%
bucks 2017
 
3.4%
bulls 2013
 
3.4%
Other values (95) 36209
60.3%
2023-07-13T22:05:45.219456image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
s 55472
14.2%
r 33354
 
8.6%
e 32606
 
8.4%
a 28475
 
7.3%
i 27546
 
7.1%
l 21088
 
5.4%
t 17020
 
4.4%
c 16339
 
4.2%
o 14017
 
3.6%
k 13492
 
3.5%
Other values (42) 130040
33.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 323786
83.1%
Uppercase Letter 59253
 
15.2%
Decimal Number 4447
 
1.1%
Space Separator 1959
 
0.5%
Other Punctuation 4
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s 55472
17.1%
r 33354
10.3%
e 32606
10.1%
a 28475
8.8%
i 27546
8.5%
l 21088
 
6.5%
t 17020
 
5.3%
c 16339
 
5.0%
o 14017
 
4.3%
k 13492
 
4.2%
Other values (14) 64377
19.9%
Uppercase Letter
ValueCountFrequency (%)
B 7944
13.4%
S 6823
11.5%
C 6476
10.9%
H 5135
8.7%
P 4773
8.1%
K 4312
7.3%
R 3835
6.5%
N 3829
6.5%
T 3735
6.3%
W 3604
6.1%
Other values (12) 8787
14.8%
Decimal Number
ValueCountFrequency (%)
7 2223
50.0%
6 2223
50.0%
3 1
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
/ 2
50.0%
' 2
50.0%
Space Separator
ValueCountFrequency (%)
1959
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 383039
98.4%
Common 6410
 
1.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
s 55472
14.5%
r 33354
 
8.7%
e 32606
 
8.5%
a 28475
 
7.4%
i 27546
 
7.2%
l 21088
 
5.5%
t 17020
 
4.4%
c 16339
 
4.3%
o 14017
 
3.7%
k 13492
 
3.5%
Other values (36) 123630
32.3%
Common
ValueCountFrequency (%)
7 2223
34.7%
6 2223
34.7%
1959
30.6%
/ 2
 
< 0.1%
' 2
 
< 0.1%
3 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 389449
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
s 55472
14.2%
r 33354
 
8.6%
e 32606
 
8.4%
a 28475
 
7.3%
i 27546
 
7.1%
l 21088
 
5.4%
t 17020
 
4.4%
c 16339
 
4.2%
o 14017
 
3.6%
k 13492
 
3.5%
Other values (42) 130040
33.4%
Distinct2473
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Memory size453.7 KiB
2023-07-13T22:05:45.499211image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length5
Median length1
Mean length2.392348371
Min length1

Characters and Unicode

Total characters138883
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique310 ?
Unique (%)0.5%

Sample

1st row-
2nd row-
3rd row-
4th row-
5th row-
ValueCountFrequency (%)
32781
56.5%
1-0 601
 
1.0%
0-1 577
 
1.0%
1-1 513
 
0.9%
2-1 396
 
0.7%
1-2 388
 
0.7%
2-2 354
 
0.6%
0-2 341
 
0.6%
2-0 303
 
0.5%
3-2 274
 
0.5%
Other values (2463) 21525
37.1%
2023-07-13T22:05:45.837530image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 58053
41.8%
1 18294
 
13.2%
2 15938
 
11.5%
3 12034
 
8.7%
4 8057
 
5.8%
0 5986
 
4.3%
5 5237
 
3.8%
6 4214
 
3.0%
7 3783
 
2.7%
8 3665
 
2.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 80830
58.2%
Dash Punctuation 58053
41.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 18294
22.6%
2 15938
19.7%
3 12034
14.9%
4 8057
10.0%
0 5986
 
7.4%
5 5237
 
6.5%
6 4214
 
5.2%
7 3783
 
4.7%
8 3665
 
4.5%
9 3622
 
4.5%
Dash Punctuation
ValueCountFrequency (%)
- 58053
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 138883
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 58053
41.8%
1 18294
 
13.2%
2 15938
 
11.5%
3 12034
 
8.7%
4 8057
 
5.8%
0 5986
 
4.3%
5 5237
 
3.8%
6 4214
 
3.0%
7 3783
 
2.7%
8 3665
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 138883
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 58053
41.8%
1 18294
 
13.2%
2 15938
 
11.5%
3 12034
 
8.7%
4 8057
 
5.8%
0 5986
 
4.3%
5 5237
 
3.8%
6 4214
 
3.0%
7 3783
 
2.7%
8 3665
 
2.6%

pts_qtr1_away
Real number (ℝ)

MISSING 

Distinct50
Distinct (%)0.1%
Missing1010
Missing (%)1.7%
Infinite0
Infinite (%)0.0%
Mean25.79866066
Minimum2
Maximum95
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size453.7 KiB
2023-07-13T22:05:45.912028image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile16
Q122
median26
Q330
95-th percentile36
Maximum95
Range93
Interquartile range (IQR)8

Descriptive statistics

Standard deviation5.844568539
Coefficient of variation (CV)0.2265454248
Kurtosis0.4147362347
Mean25.79866066
Median Absolute Deviation (MAD)4
Skewness0.14690344
Sum1471633
Variance34.15898141
MonotonicityNot monotonic
2023-07-13T22:05:45.969588image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
26 3990
 
6.9%
24 3900
 
6.7%
25 3809
 
6.6%
27 3693
 
6.4%
28 3613
 
6.2%
22 3466
 
6.0%
23 3412
 
5.9%
29 3260
 
5.6%
30 3010
 
5.2%
21 2735
 
4.7%
Other values (40) 22155
38.2%
ValueCountFrequency (%)
2 1
 
< 0.1%
3 1
 
< 0.1%
5 2
 
< 0.1%
6 6
 
< 0.1%
7 18
< 0.1%
ValueCountFrequency (%)
95 1
 
< 0.1%
53 3
 
< 0.1%
50 4
 
< 0.1%
49 4
 
< 0.1%
48 10
< 0.1%

pts_qtr2_away
Text

MISSING 

Distinct56
Distinct (%)0.1%
Missing1013
Missing (%)1.7%
Memory size453.7 KiB
2023-07-13T22:05:46.083720image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length4
Median length2
Mean length1.998597475
Min length1

Characters and Unicode

Total characters114000
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9 ?
Unique (%)< 0.1%

Sample

1st row15
2nd row12
3rd row10
4th row12
5th row23
ValueCountFrequency (%)
26 3951
 
6.9%
24 3912
 
6.9%
25 3910
 
6.9%
27 3730
 
6.5%
23 3700
 
6.5%
28 3451
 
6.1%
22 3387
 
5.9%
29 3101
 
5.4%
21 3038
 
5.3%
30 2815
 
4.9%
Other values (46) 22045
38.6%
2023-07-13T22:05:46.267656image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 40615
35.6%
3 18892
16.6%
1 14045
 
12.3%
4 6326
 
5.5%
6 5747
 
5.0%
8 5741
 
5.0%
0 5736
 
5.0%
5 5726
 
5.0%
7 5634
 
4.9%
9 5537
 
4.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 113999
> 99.9%
Other Punctuation 1
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 40615
35.6%
3 18892
16.6%
1 14045
 
12.3%
4 6326
 
5.5%
6 5747
 
5.0%
8 5741
 
5.0%
0 5736
 
5.0%
5 5726
 
5.0%
7 5634
 
4.9%
9 5537
 
4.9%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 114000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 40615
35.6%
3 18892
16.6%
1 14045
 
12.3%
4 6326
 
5.5%
6 5747
 
5.0%
8 5741
 
5.0%
0 5736
 
5.0%
5 5726
 
5.0%
7 5634
 
4.9%
9 5537
 
4.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 114000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 40615
35.6%
3 18892
16.6%
1 14045
 
12.3%
4 6326
 
5.5%
6 5747
 
5.0%
8 5741
 
5.0%
0 5736
 
5.0%
5 5726
 
5.0%
7 5634
 
4.9%
9 5537
 
4.9%

pts_qtr3_away
Text

MISSING 

Distinct55
Distinct (%)0.1%
Missing1046
Missing (%)1.8%
Memory size453.7 KiB
2023-07-13T22:05:46.396830image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length4
Median length2
Mean length1.998158121
Min length1

Characters and Unicode

Total characters113909
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)< 0.1%

Sample

1st row17
2nd row18
3rd row10
4th row19
5th row14
ValueCountFrequency (%)
24 3806
 
6.7%
26 3778
 
6.6%
25 3734
 
6.6%
27 3668
 
6.4%
23 3606
 
6.3%
28 3433
 
6.0%
22 3262
 
5.7%
29 3118
 
5.5%
21 2890
 
5.1%
30 2797
 
4.9%
Other values (45) 22915
40.2%
2023-07-13T22:05:46.578037image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 39592
34.8%
3 19234
16.9%
1 14377
 
12.6%
4 6479
 
5.7%
8 5757
 
5.1%
6 5748
 
5.0%
0 5746
 
5.0%
7 5701
 
5.0%
5 5640
 
5.0%
9 5633
 
4.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 113907
> 99.9%
Other Punctuation 2
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 39592
34.8%
3 19234
16.9%
1 14377
 
12.6%
4 6479
 
5.7%
8 5757
 
5.1%
6 5748
 
5.0%
0 5746
 
5.0%
7 5701
 
5.0%
5 5640
 
5.0%
9 5633
 
4.9%
Other Punctuation
ValueCountFrequency (%)
. 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 113909
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 39592
34.8%
3 19234
16.9%
1 14377
 
12.6%
4 6479
 
5.7%
8 5757
 
5.1%
6 5748
 
5.0%
0 5746
 
5.0%
7 5701
 
5.0%
5 5640
 
5.0%
9 5633
 
4.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 113909
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 39592
34.8%
3 19234
16.9%
1 14377
 
12.6%
4 6479
 
5.7%
8 5757
 
5.1%
6 5748
 
5.0%
0 5746
 
5.0%
7 5701
 
5.0%
5 5640
 
5.0%
9 5633
 
4.9%

pts_qtr4_away
Real number (ℝ)

MISSING 

Distinct54
Distinct (%)0.1%
Missing1046
Missing (%)1.8%
Infinite0
Infinite (%)0.0%
Mean25.58438437
Minimum0
Maximum58
Zeros3
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size453.7 KiB
2023-07-13T22:05:46.646913image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile16
Q121
median25
Q330
95-th percentile36
Maximum58
Range58
Interquartile range (IQR)9

Descriptive statistics

Standard deviation6.054387832
Coefficient of variation (CV)0.236643874
Kurtosis0.07867993152
Mean25.58438437
Median Absolute Deviation (MAD)4
Skewness0.1462758462
Sum1458489
Variance36.65561202
MonotonicityNot monotonic
2023-07-13T22:05:46.705554image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
26 3813
 
6.6%
24 3706
 
6.4%
25 3698
 
6.4%
23 3586
 
6.2%
27 3578
 
6.2%
28 3490
 
6.0%
29 3179
 
5.5%
22 3172
 
5.5%
21 2930
 
5.0%
30 2700
 
4.7%
Other values (44) 23155
39.9%
ValueCountFrequency (%)
0 3
< 0.1%
1 1
 
< 0.1%
3 1
 
< 0.1%
4 2
< 0.1%
5 2
< 0.1%
ValueCountFrequency (%)
58 1
 
< 0.1%
54 1
 
< 0.1%
53 4
< 0.1%
51 1
 
< 0.1%
50 4
< 0.1%

pts_ot1_away
Real number (ℝ)

MISSING  ZEROS 

Distinct25
Distinct (%)0.1%
Missing25759
Missing (%)44.4%
Infinite0
Infinite (%)0.0%
Mean1.034185917
Minimum0
Maximum24
Zeros29008
Zeros (%)50.0%
Negative0
Negative (%)0.0%
Memory size453.7 KiB
2023-07-13T22:05:46.760224image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile10
Maximum24
Range24
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3.305070329
Coefficient of variation (CV)3.195818349
Kurtosis10.23777124
Mean1.034185917
Median Absolute Deviation (MAD)0
Skewness3.292551995
Sum33398
Variance10.92348988
MonotonicityNot monotonic
2023-07-13T22:05:46.811532image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
0 29008
50.0%
8 363
 
0.6%
10 341
 
0.6%
12 307
 
0.5%
11 291
 
0.5%
9 291
 
0.5%
6 253
 
0.4%
7 247
 
0.4%
13 240
 
0.4%
14 195
 
0.3%
Other values (15) 758
 
1.3%
(Missing) 25759
44.4%
ValueCountFrequency (%)
0 29008
50.0%
1 6
 
< 0.1%
2 31
 
0.1%
3 40
 
0.1%
4 127
 
0.2%
ValueCountFrequency (%)
24 1
 
< 0.1%
23 3
 
< 0.1%
22 9
< 0.1%
21 10
< 0.1%
20 16
< 0.1%

pts_ot2_away
Real number (ℝ)

MISSING  ZEROS 

Distinct20
Distinct (%)0.1%
Missing27051
Missing (%)46.6%
Infinite0
Infinite (%)0.0%
Mean0.1498612993
Minimum0
Maximum21
Zeros30522
Zeros (%)52.6%
Negative0
Negative (%)0.0%
Memory size453.7 KiB
2023-07-13T22:05:46.861781image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum21
Range21
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.274237542
Coefficient of variation (CV)8.502779225
Kurtosis91.32575989
Mean0.1498612993
Median Absolute Deviation (MAD)0
Skewness9.272602309
Sum4646
Variance1.623681314
MonotonicityNot monotonic
2023-07-13T22:05:46.907447image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
0 30522
52.6%
10 64
 
0.1%
9 52
 
0.1%
7 49
 
0.1%
8 48
 
0.1%
12 37
 
0.1%
13 36
 
0.1%
6 36
 
0.1%
11 33
 
0.1%
14 26
 
< 0.1%
Other values (10) 99
 
0.2%
(Missing) 27051
46.6%
ValueCountFrequency (%)
0 30522
52.6%
2 6
 
< 0.1%
3 6
 
< 0.1%
4 23
 
< 0.1%
5 19
 
< 0.1%
ValueCountFrequency (%)
21 1
 
< 0.1%
19 4
 
< 0.1%
18 4
 
< 0.1%
17 9
< 0.1%
16 12
< 0.1%

pts_ot3_away
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct18
Distinct (%)0.1%
Missing27243
Missing (%)46.9%
Infinite0
Infinite (%)0.0%
Mean0.02440765985
Minimum0
Maximum20
Zeros30732
Zeros (%)52.9%
Negative0
Negative (%)0.0%
Memory size453.7 KiB
2023-07-13T22:05:46.954971image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum20
Range20
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.5209009241
Coefficient of variation (CV)21.34169877
Kurtosis605.7078299
Mean0.02440765985
Median Absolute Deviation (MAD)0
Skewness23.67367373
Sum752
Variance0.2713377727
MonotonicityNot monotonic
2023-07-13T22:05:47.004324image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
0 30732
52.9%
7 10
 
< 0.1%
8 8
 
< 0.1%
10 8
 
< 0.1%
11 8
 
< 0.1%
6 6
 
< 0.1%
12 5
 
< 0.1%
15 5
 
< 0.1%
14 5
 
< 0.1%
9 5
 
< 0.1%
Other values (8) 18
 
< 0.1%
(Missing) 27243
46.9%
ValueCountFrequency (%)
0 30732
52.9%
2 2
 
< 0.1%
3 1
 
< 0.1%
4 3
 
< 0.1%
5 4
 
< 0.1%
ValueCountFrequency (%)
20 1
 
< 0.1%
17 2
 
< 0.1%
16 2
 
< 0.1%
15 5
< 0.1%
14 5
< 0.1%

pts_ot4_away
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct10
Distinct (%)< 0.1%
Missing27270
Missing (%)47.0%
Infinite0
Infinite (%)0.0%
Mean0.005977325147
Minimum0
Maximum20
Zeros30767
Zeros (%)53.0%
Negative0
Negative (%)0.0%
Memory size453.7 KiB
2023-07-13T22:05:47.054937image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum20
Range20
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.2725682003
Coefficient of variation (CV)45.60036364
Kurtosis2604.817578
Mean0.005977325147
Median Absolute Deviation (MAD)0
Skewness49.20775414
Sum184
Variance0.07429342382
MonotonicityNot monotonic
2023-07-13T22:05:47.100956image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0 30767
53.0%
11 4
 
< 0.1%
12 2
 
< 0.1%
8 2
 
< 0.1%
9 2
 
< 0.1%
13 2
 
< 0.1%
14 1
 
< 0.1%
6 1
 
< 0.1%
16 1
 
< 0.1%
20 1
 
< 0.1%
(Missing) 27270
47.0%
ValueCountFrequency (%)
0 30767
53.0%
6 1
 
< 0.1%
8 2
 
< 0.1%
9 2
 
< 0.1%
11 4
 
< 0.1%
ValueCountFrequency (%)
20 1
< 0.1%
16 1
< 0.1%
14 1
< 0.1%
13 2
< 0.1%
12 2
< 0.1%

pts_ot5_away
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct2
Distinct (%)< 0.1%
Missing45577
Missing (%)78.5%
Infinite0
Infinite (%)0.0%
Mean0.001362616223
Minimum0
Maximum17
Zeros12475
Zeros (%)21.5%
Negative0
Negative (%)0.0%
Memory size453.7 KiB
2023-07-13T22:05:47.147417image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum17
Range17
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.1521988035
Coefficient of variation (CV)111.696016
Kurtosis12476
Mean0.001362616223
Median Absolute Deviation (MAD)0
Skewness111.696016
Sum17
Variance0.02316447579
MonotonicityDecreasing
2023-07-13T22:05:47.187694image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
0 12475
 
21.5%
17 1
 
< 0.1%
(Missing) 45577
78.5%
ValueCountFrequency (%)
0 12475
21.5%
17 1
 
< 0.1%
ValueCountFrequency (%)
17 1
 
< 0.1%
0 12475
21.5%

pts_ot6_away
Real number (ℝ)

CONSTANT  MISSING  ZEROS 

Distinct1
Distinct (%)< 0.1%
Missing45578
Missing (%)78.5%
Infinite0
Infinite (%)0.0%
Mean0
Minimum0
Maximum0
Zeros12475
Zeros (%)21.5%
Negative0
Negative (%)0.0%
Memory size453.7 KiB
2023-07-13T22:05:47.232264image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum0
Range0
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0
Coefficient of variation (CV)nan
Kurtosis0
Mean0
Median Absolute Deviation (MAD)0
Skewness0
Sum0
Variance0
MonotonicityIncreasing
2023-07-13T22:05:47.271082image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=1)
ValueCountFrequency (%)
0 12475
 
21.5%
(Missing) 45578
78.5%
ValueCountFrequency (%)
0 12475
21.5%
ValueCountFrequency (%)
0 12475
21.5%

pts_ot7_away
Real number (ℝ)

CONSTANT  MISSING  ZEROS 

Distinct1
Distinct (%)< 0.1%
Missing45578
Missing (%)78.5%
Infinite0
Infinite (%)0.0%
Mean0
Minimum0
Maximum0
Zeros12475
Zeros (%)21.5%
Negative0
Negative (%)0.0%
Memory size453.7 KiB
2023-07-13T22:05:47.311967image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum0
Range0
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0
Coefficient of variation (CV)nan
Kurtosis0
Mean0
Median Absolute Deviation (MAD)0
Skewness0
Sum0
Variance0
MonotonicityIncreasing
2023-07-13T22:05:47.350722image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=1)
ValueCountFrequency (%)
0 12475
 
21.5%
(Missing) 45578
78.5%
ValueCountFrequency (%)
0 12475
21.5%
ValueCountFrequency (%)
0 12475
21.5%

pts_ot8_away
Real number (ℝ)

CONSTANT  MISSING  ZEROS 

Distinct1
Distinct (%)< 0.1%
Missing45578
Missing (%)78.5%
Infinite0
Infinite (%)0.0%
Mean0
Minimum0
Maximum0
Zeros12475
Zeros (%)21.5%
Negative0
Negative (%)0.0%
Memory size453.7 KiB
2023-07-13T22:05:47.391531image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum0
Range0
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0
Coefficient of variation (CV)nan
Kurtosis0
Mean0
Median Absolute Deviation (MAD)0
Skewness0
Sum0
Variance0
MonotonicityIncreasing
2023-07-13T22:05:47.430248image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=1)
ValueCountFrequency (%)
0 12475
 
21.5%
(Missing) 45578
78.5%
ValueCountFrequency (%)
0 12475
21.5%
ValueCountFrequency (%)
0 12475
21.5%

pts_ot9_away
Real number (ℝ)

CONSTANT  MISSING  ZEROS 

Distinct1
Distinct (%)< 0.1%
Missing45578
Missing (%)78.5%
Infinite0
Infinite (%)0.0%
Mean0
Minimum0
Maximum0
Zeros12475
Zeros (%)21.5%
Negative0
Negative (%)0.0%
Memory size453.7 KiB
2023-07-13T22:05:47.471444image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum0
Range0
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0
Coefficient of variation (CV)nan
Kurtosis0
Mean0
Median Absolute Deviation (MAD)0
Skewness0
Sum0
Variance0
MonotonicityIncreasing
2023-07-13T22:05:47.510249image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=1)
ValueCountFrequency (%)
0 12475
 
21.5%
(Missing) 45578
78.5%
ValueCountFrequency (%)
0 12475
21.5%
ValueCountFrequency (%)
0 12475
21.5%

pts_ot10_away
Real number (ℝ)

CONSTANT  MISSING  ZEROS 

Distinct1
Distinct (%)< 0.1%
Missing45578
Missing (%)78.5%
Infinite0
Infinite (%)0.0%
Mean0
Minimum0
Maximum0
Zeros12475
Zeros (%)21.5%
Negative0
Negative (%)0.0%
Memory size453.7 KiB
2023-07-13T22:05:47.550724image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum0
Range0
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0
Coefficient of variation (CV)nan
Kurtosis0
Mean0
Median Absolute Deviation (MAD)0
Skewness0
Sum0
Variance0
MonotonicityIncreasing
2023-07-13T22:05:47.589984image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=1)
ValueCountFrequency (%)
0 12475
 
21.5%
(Missing) 45578
78.5%
ValueCountFrequency (%)
0 12475
21.5%
ValueCountFrequency (%)
0 12475
21.5%

pts_away
Real number (ℝ)

Distinct130
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean102.6175391
Minimum18
Maximum186
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size453.7 KiB
2023-07-13T22:05:47.641162image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile79
Q193
median103
Q3112
95-th percentile127
Maximum186
Range168
Interquartile range (IQR)19

Descriptive statistics

Standard deviation14.61687927
Coefficient of variation (CV)0.1424403605
Kurtosis0.3769442719
Mean102.6175391
Median Absolute Deviation (MAD)10
Skewness0.01115675737
Sum5957256
Variance213.6531596
MonotonicityNot monotonic
2023-07-13T22:05:47.698177image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
102 1663
 
2.9%
100 1640
 
2.8%
103 1624
 
2.8%
106 1615
 
2.8%
105 1613
 
2.8%
104 1600
 
2.8%
99 1576
 
2.7%
98 1563
 
2.7%
96 1551
 
2.7%
101 1543
 
2.7%
Other values (120) 42065
72.5%
ValueCountFrequency (%)
18 1
< 0.1%
33 2
< 0.1%
38 1
< 0.1%
43 1
< 0.1%
44 2
< 0.1%
ValueCountFrequency (%)
186 1
< 0.1%
184 2
< 0.1%
182 2
< 0.1%
178 2
< 0.1%
173 1
< 0.1%